Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 10 min ago
Peer-reviewedCC BY 4.0Fractal and Fractional

Fractional-Order Dynamic Illumination Adjustment for Low-Light Enhancement Using Luminance Segmentation

  • Bao Chen1
  • Tianlan Xiong1
  • Juanjuan Gao2
  • Yuchao Tang3
  1. 1School of Mathematics and Information Sciences, Nanchang Hangkong University, Nanchang 330063, China
  2. 2School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510002, China
  3. 3School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
Open at sourceDOICite

Abstract

Low-light image enhancement is an important problem in image processing. In this paper, we propose a novel adaptive variational model based on fractional-order gradients for low-light image enhancement. Specifically, the low-light part of the low-light image is enhanced, that is, the contrast of the low-light image is enhanced. The core concept of the proposed technique involves partitioning the image domain into low-light and brighter regions by applying an optimal threshold value. Then, a fitting term is further used for the low-light parts, so that the pixels in the low-light area can be enhanced by adjusting the contrast and brightness parameters. The existence of a minimizer is established under the stated assumptions, and the uniqueness of the solution is proved under the corresponding convexity conditions. An efficient split Bregman method is developed to solve the proposed variational model. Experimental results demonstrate the effectiveness of the proposed method for low-light image enhancement.

Related

  1. Fractional illumination and the optimal exponential rate in Hadwiger's covering conjecture
    Yegor Gorodzha
    arXivPreprintarXiv licenceFull textCombinatorics & discrete mathematicsGeometry & topologyarXiv
  2. DiBME-Net: Mirror-Boundary-Guided Diffusion-Band Feature Enhancement for RGB-D Mirror Segmentation
    Seokkyeong Boo, Yunjae Cheon, Yong Ju Jung
    MathematicsPeer-reviewedCC BY 4.0General mathematicsCrossref
  3. GPU-Accelerated Robust MA--RIS Pre-Optimization for Communication and Target Illumination
    Tran Le Dung, Ho Viet Duc Luong
    arXivPreprintCC BY 4.0Full textGeneral mathematicsarXiv
  4. Regression adjustment in completely randomized experiments with many covariates
    Harold D. Chiang, Yukitoshi Matsushita, Taisuke Otsu
    Journal of EconometricsPeer-reviewedLicence not stated13 citedStatisticsOpenAlex
  5. Automated, Efficient, and Model-Free Inference for Randomized Clinical Trials via Data-Driven Covariate Adjustment
    Kelly Van Lancker, Ivan Matos Diaz, Stijn Vansteelandt
    The American StatisticianPeer-reviewedLicence not statedFull text15 citedStatisticsOpenAlex

Cite

BibTeXRIS
BibTeX
@article{chen2026fractionalorder,
  title = {{Fractional-Order Dynamic Illumination Adjustment for Low-Light Enhancement Using Luminance Segmentation}},
  author = {Bao Chen and Tianlan Xiong and Juanjuan Gao and Yuchao Tang},
  journal = {Fractal and Fractional},
  year = {2026},
  month = oct,
  doi = {10.3390/fractalfract10110755},
  url = {https://www.mdpi.com/2504-3110/10/11/755},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Fractional-Order Dynamic Illumination Adjustment for Low-Light Enhancement Using Luminance Segmentation
AU  - Bao Chen
AU  - Tianlan Xiong
AU  - Juanjuan Gao
AU  - Yuchao Tang
PY  - 2026
DA  - 2026/10/28
JO  - Fractal and Fractional
DO  - 10.3390/fractalfract10110755
UR  - https://www.mdpi.com/2504-3110/10/11/755
AB  - Low-light image enhancement is an important problem in image processing. In this paper, we propose a novel adaptive variational model based on fractional-order gradients for low-light image enhancement. Specifically, the low-light part of the low-light image is enhanced, that is, the contrast of the low-light image is enhanced. The core concept of the proposed technique involves partitioning the image domain into low-light and brighter regions by applying an optimal threshold value. Then, a fitting term is further used for the low-light parts, so that the pixels in the low-light area can be enhanced by adjusting the contrast and brightness parameters. The existence of a minimizer is established under the stated assumptions, and the uniqueness of the solution is proved under the corresponding convexity conditions. An efficient split Bregman method is developed to solve the proposed variational model. Experimental results demonstrate the effectiveness of the proposed method for low-light image enhancement.
SN  - 2504-3110
ER  -